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This repository lists IDS Deployment Scenarios from various domains and cases. It serves as a library of information, listing different deployment alternatives applied by IDS projects. The ultimate aim is to create a source of inspiration and guidance for those who wish to experiment with data spaces or search for the best deployment practices. This repository also serves as a platform where everyone can express their opinions on the existing deployment scenarios by creating issues and initiating discussions around them.
An example deployment of Minimum Viable Data Space (as defined by IDSA)
CA, DAPS and Connector
How to deploy an IDS-Testbed using Kubernetes
CA, DAPS and Connector
EDC deployment based on Confidential Computing principles. This shows how Intel's Software Guard Extensions can be utilized to set up a Minimal Viable Dataspace (MVD) without fundamental changes to the code of the business logic.
Eclipse Dataspace Components
🚀 Minimum Viable Data Space is also included as a deployment scenario, defined by IDSA Head Office. It is also a great place to start creating a deployment scenario.
An IDS deployment scenario is a description of the steps and processes involved in delivering a component or a set of components to end-users. It outlines the various stages of deployment, from development and testing to release and optionally maintenance. The scenario typically includes information on the prerequisites (such as hardware and software requirements), configuration settings, and any necessary third-party integrations. It also includes details on the deployment environment, such as whether it will be installed on-premises or in the cloud, and how the system will be monitored and supported once it is live. A deployment scenario helps ensure that the deployment process is consistent, easily repeatable, reusable and efficient, and that the system is deployed in a way that meets the needs of end-users.
A deployment scenario can be considered as any implementation made with IDS-compliant components:
that allows sovereign data sharing (as defined by IDSA)
is built with a clear purpose, to solve a problem
is adequately documented to enable others to follow the same path
As depicted on the image above, there are several ways to create a deployment scenario. An IDS Deployment Scenario goes beyond just implementing solutions that comply with IDS. It's also about leveraging the latest tools and technologies like Artificial Intelligence (AI), Analytics, ETL tools, and more. The aim is to solve a problem comprehensively, not just through the use of IDS, but also by combining other tools and services. Therefore, it's crucial to address key questions such as how the integration was achieved, the challenges that were encountered, resolved and the benefits gained from incorporating additional tools and services. A successful deployment scenario must involve a holistic approach that leverages various technologies and tools to create a comprehensive solution that delivers optimal results.
An envisaged flow for running an experiment with a IDS Deployment Scenario can be considered as depicted on the image below:
We are currently in the early stages of data spaces technology. Although the concept has been around for a few years, it is possible to say that it is still relatively new and under continuous development. Data spaces offer a range of benefits, including improved collaboration, increased efficiency, and better decision-making. However, the technology is still evolving, and there are many challenges that need to be addressed, such as data privacy and security concerns, interoperability issues, and the need for standardized protocols. IDS Rulebook provides great guidance here to help with these processes. As a result, data spaces are still in the early-adoption phase, with only a minority exploring the technology's potential, and on the other hand, a vast majority looking for best practices.
In all technologies in this early-adoption phase, sharing experiments and deployment scenarios play a crucial role in the technology's evolution. The sharing of best practices and deployment scenarios is particularly important in the case of data spaces, as it is a complex and evolving technology. Sharing deployment scenarios can help others learn from your experiences and avoid common mistakes. It can also facilitate the creation of standards and best practices, which can help drive the adoption and development of data spaces. By sharing their experiences and best practices, early adopters can help build a strong and collaborative community of data space practitioners, which will be critical to the success of this emerging technology.
Sharing your deployment scenario with others is an essential part of the open source philosophy, which is based on the idea that collaboration and sharing knowledge leads to better results. By sharing your deployment scenario, you allow others to learn from your experience and benefit from your insights, potentially saving them time and effort in their own deployment process.
Sharing deployment scenarios with others can also have many advantages for the original implementor, including:
Getting recognized: Sharing deployment scenarios with others can help the original implementor get recognized for their work. By sharing their scenarios, they can showcase their expertise and demonstrate their ability to solve complex problems.
Feedback and improvement: When others use the deployment scenarios, they may provide feedback and suggest improvements. This can help the original implementor refine their work and make it even better.
Collaboration: Sharing deployment scenarios with others can foster collaboration and create a community of people who are interested in similar topics. This can lead to new partnerships, joint projects, and opportunities to learn from others.
Feel good factor: Sharing deployment scenarios with others can give the original implementor a sense of satisfaction and fulfillment. Knowing that their work is helping others and making a positive impact can be very rewarding.
In the Data Space Radar, all solutions that reached the pilot and live stages can be considered as an IDS Deployment Scenario, since they involve the implementation of a data space solution to solve a specific business problem.
The deployment scenarios listed here are not necessarily attached to a particular use case in the Data Space Radar. The relationship of IDS Deployment Scenarios with other assets of IDSA is explained in more detail in the Deployment Scenarios Report.
You are warmly invited to contribute to the IDS Deployment Scenarios in two ways:
🚩 By sending a pull request via this repository (please be sure to check the contribution guidelines).
🚩 You can also share it via this form for the Data Space Radar. While filling in the form, please note that only the implementations at the pilot and live stages will be considered as IDS Deployment Scenarios.
While there is no strict template to adhere to when sharing deployment scenarios with others, we have some recommendations that can be taken into consideration to ensure that the sharing process is effective and helpful.
We recommend that your deployment scenario to contain:
System Requirements: Specify the minimum hardware and software requirements necessary for the successful deployment of the system.
If any network configuration is required.
Deployment diagram (to show which environment each component is deployed) Docker, Kubernetes Server, etc. Describe the overall architecture of the system, including any third-party components or integrations.
Which components are used in the deployment scenario.
What purpose the entire deployment is made for? What problem it tries to solve? Please clearly define the purpose of the deployment scenario, including the intended use case and expected outcomes.
Deployment Process Describe the deployment process, including any installation, configuration, or testing procedures.
Any other resources that might be helpful for a third party to reproduce the scenario.
Couldn't find the information you were looking for? Please proceed to our FAQ page, where we delve deeper into more specific questions and provide detailed answers to assist with more complex scenarios around IDS Deployment Scenarios.
